he molecular biology revolution has allowed scientists for the past 30 years to analyze in great detail single genes and their contributions to cellular processes. Although the method has provided enormous insights into the biochemistry of life, genes dont operate in isolation. The collection of genes in a cell helps define that cells behavior.

A relatively new methodology, called whole genome gene expression analysis that employs microarray technology, allows researchers to take a snapshot of many genes acting in cells, be they normal cells, diseased ones, or ones treated with drugs. More and more Columbia Health Sciences investigators are employing these microarrays to advance cancer, diabetes, and neurobiological research. Others are improving the technology and analytical tools themselves, which were developed elsewhere in the mid-1990s but have come into wider use here during the past two years.

Helping to coordinate the microarray effort at Health Sciences is the Columbia Genome Center, which offers guidance on how best to use the research tool. Center staff can steer interested researchers to the two microarray facilities, one in the Institute for Cancer Genetics and the other jointly operated by the Naomi Berrie Diabetes Center and the Genome Center. Besides helping researchers design experiments, some Genome Center scientists can assist researchers in interpreting the mounds of data generated by the research.

Two kinds of microarrays for gene expression are available at Columbia: spotted arrays, which are produced on campus, and GeneChip arrays, which are sold by Affymetrix of Santa Clara, Calif. With a spotted array, 1,000 to 10,000 different cDNAs are attached to a glass slide. Labeled cDNAs from two tissues of interest are then hybridized to a single microarray (see illustration). The Affymetrix system puts approximately 400,000 different oligonucleotides on each DNA chip to represent up to 12,000 genes, with cDNAs from control tissue put on one Affy-metrix chip and cDNAs from the experimental tissue on another chip. In both methods, lasers scan the slide or chip and software analyzes the complex pattern of gene expression generated.

The cancer genetics facility uses Affymetrix gene chips while the diabetes center has a spotted array system. The two methods can be complementary but one method may be more appropriate, depending on the research question.

Produced under stringent manufacturing standards, Affymetrix chips are generally considered more reliable than spotted arrays, which scientists can make in their labs. Spotted arrays, however, enable researchers to design a slide to study a specific set of genes or gene transcripts that may not be available on a standard gene chip.

A downside to microarray research remains cost, although the price of the technology has gone down. Columbia researchers, for example, can obtain the Affymetrix chips at an 85 percent discount, for $350 each. Experiments can use many chips, though.

The laboratory of Dr. Riccardo Dalla-Favera, professor of pathology and of genetics & development and director of the Institute for Cancer Genetics, is using Affymetrix microarrays to study human B cell malignancies. "We want to identify genes involved in the oncogenic transformation from a normal B cell to a tumor cell," says Dr. Ulf Klein, a postdoc in Dr. Dalla-Faveras laboratory and lead author of a paper in the Dec. 3, 2001, issue of the Journal of Experimental Medicine.

The microarray study found that a form of leukemia with two subtypes that differ both genetically and clinically but have similar looking tumor cells are actually derived from only one type of normal B cell. The gene expression pattern might be used in diagnosis, if other methods lead to inconclusive results. Also, genes may be identified that could become targets for novel therapeutic approaches.

Dr. Rudolph Leibel, professor of pediatrics and medicine and co-director of the Naomi Berrie Diabetes Center, and Dr. Anthony Ferrante, instructor in the Department of Medicines nutrition and preventive medicine division and in the diabetes center, have been using both microarray types to study obesity and insulin resistance in diabetes. Insulin resistance, which often occurs in obese people and is a hallmark of type 2 diabetes mellitus, is a decreased sensitivity to insulin. "Were trying to identify genes whose expression correlates with insulin resistance," Dr. Ferrante says.

A recent study by diabetes center researchers published in the October 2001 issue of Diabetes used microarrays and showed how leptin, a hormone that regulates feeding and energy use, could affect the expression of certain liver genes in obese mice. Without leptin, mice are severely insulin-resistant. By replacing leptin in leptin-deficient mice, the researchers identified leptin-regulated liver genes, including a subset involved in insulin resistance.

Dr. Etienne Sibille, a neuroscience investigator at the New York State Psychiatric Institute, hopes to find genes implicated in anxiety, depression, and suicide. Using Affymetrix chips, his long-term goal is to identify the genes in human patients and then create mouse models of the diseases with mutations in the genes. For now, he is analyzing microarray expression data generated from post-mortem human brain samples.

In a related microarray project, Dr. Conrad Gilliam, professor of genetics & development and director of the Genome Center, and Dr. Eric Kandel, Univer-sity Professor in the Center for Neurobiology and Behavior, are studying fear conditioning and working memory in mice to gain insight into the molecular genetic basis of anxiety disorders and schizophrenia.

Genome Center researchers are also developing novel methods to analyze gene expression data, detect gene promoter sequences, and bring statistical standards to gene expression analysis. In the September 2001 issue of Genome Biology, Dr. Paul Pavlidis, associate research scientist, and Dr. William Noble, assistant professor of computer science with a joint appointment in the Genome Center, described statistical approaches that increase the power to detect differences in gene expression in complex datasets. In the February 2001 issue of Nature Genetics, Dr. Harmen Bussemaker, assistant professor of biology with a joint appointment in the Genome Center, reported a novel method to predict regulatory elements based upon correlated gene expression.

"Tackling the complexity of what every gene is doing at once is going to be necessary if we are going to gain a real understanding of cellular biology," Dr. Pavlidis says. "Micro-arrays represent one of the keys to a major goal of biology: a complete model of a cell that lets us accurately predict the effects of things like mutations or drugs."